Machine Learning-Based Investigation on the Impact of Chinese Venture Capital Institutions’ Performance: Evaluation Factors of Venture Enterprises to Venture Capital Institutions

Hu, Jitao; Hu, Longying; Hu, Mingzhu; He, Qiuzhi

doi:10.3390/systems10040092

Cited by 3 publications

(2 citation statements)

References 31 publications

(27 reference statements)

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“…of venture capital-backed firms spending and patenting activities. Additionally, they offer value-added services, monitor and provide resource connections, offer better management and employee incentives, provide wider networks of product-market contacts and assist in the recruitment of skilled executives (Inkpen and Dinur, 1998;Hellmann, 1998;Kortum and Lerner, 2001;Hellmann and Puri, 2002;Hall and Lerner, 2010;Sun et al, 2019;Khurshed et al, 2020;Hu et al, 2022;Jain and Kini, 2022;MacMillan et al, 2022). Much has been written about the contribution of VC to innovation and firm performance.…”

Section: Performancementioning

confidence: 99%

The effects of organizational learning on innovation and performance of venture capital-backed firms: the moderating effect of syndication

Laachach

Ettahri

2023

BPMJ

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PurposeDespite the innovation and performance of venture capital (VC)-backed firms receiving extensive attention, how and under what conditions VC influences innovation and performance remains unclear. The present paper draws on organizational learning (OL) theory to examine the moderating effect of syndication on backed firms.Design/methodology/approachDrawing on a literature review that connects OL and innovation to the performance of VC-backed firms, this study examines the effects of OL on innovation and firm performance among these firms by questioning the moderating effect of VC syndication. A sample of 78 VC-backed firms was used to test the robustness of the proposed model and causal relationships through the use of partial least squares structural equation modeling (PLS-SEM).FindingsThe empirical evidence demonstrates that the intervention of venture capitalists can not only stimulate innovation, but also have a significantly positive effect on firm performance. Furthermore, the evidence reveals that syndication of VC investment supports backed firms in improving the firms' performance and generating innovation from acquired knowledge.Originality/valueTo the best of the authors' knowledge, this study is the first in North Africa that focuses on the moderating effects of venture capital syndication on the relationships between OL, innovation and firm performance.

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Section: Performancementioning

confidence: 99%

The effects of organizational learning on innovation and performance of venture capital-backed firms: the moderating effect of syndication

Laachach

Ettahri

2023

BPMJ

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“…With the development of emerging technologies, machine learning has gained much attention for its superior performance in traditional industries, especially in data processing [27,28]. Based on the differences in domestic and foreign institutional systems, the difficulty of learning from foreign experiences, the controversies about the necessity of mixed reform of SOEs in China, and the lack of attention to employee stock ownership as a mixed reform strategy, this paper innovatively constructs a model based on machine learning algorithms to explore the effects of mixed reform strategies on agency costs of SOEs, and identifies the optimal ratio settings of mixed reform strategy indicators while testing the effectiveness of policies.…”

Section: Introductionmentioning

confidence: 99%

Research on the Effectiveness of Deep Learning−Based Agency Cost Suppression Strategy: A Case Study of State−Owned Enterprises in Mainland China

Zhai

Zhao

Bai

et al. 2022

Systems

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The mixed ownership reform aims to improve the property rights structure of the state−owned enterprises (SOEs) and reduce agency costs, and the current mixed reform strategies mainly include equity blending by introducing external non−state capital, executive assignments, and employee stock ownership. In this paper, 953 valid data of A−shares listed in Shanghai and Shenzhen from 2008 to 2020 are used as samples to construct the indicators of mixed reform strategy by the literature statistics method. After obtaining multiple impact indicators, the regression impact model of corporate agency cost suppression strategy is constructed by MATLAB software using a machine learning algorithm. On this basis, the performance of multiple machine learning algorithms is compared, and it is found that the integrated optimization−based bag−boosting model is used to study the effect of hybrid reform strategy to reduce the agency costs of SOEs, and the proportional setting of indicators when the effect is optimal is also explored. Finally, the laws of different influencing factors on the agency costs of enterprises are explored separately by the eigenvalue method. The results of the study show that the proportion of shareholding of the first largest non−state shareholder is sin−functional with the agency costs of SOEs when non−state majority shareholders are introduced into SOEs’ equity mix, and the agency costs tend to decrease after SOEs become privately held enterprises. The greater the number and proportion of supervisors appointed by non−state shareholders, the greater the supervisory restraint effect on SOE managers and the better the effect of suppressing agency costs. The participation of non−state−owned shareholders in the company’s business decisions by appointed executives and the special resource advantages of SOEs intensify the occurrence of the self−interest of appointed executives and the increase of agency costs of SOEs. The implementation of an employee stock ownership plan plays the role of employee supervision and restraint on SOE managers, which reduces the agency costs of SOEs. Based on this, it can provide support for the government to improve the hybrid reform policy and promote the process layer by layer, and also provide theoretical reference for SOEs to deepen the equity mix, incentivize employee shareholding, and empower non−state shareholders to govern and thus reduce agency costs.

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Effective Evaluation of Green and High-Quality Development Capabilities of Enterprises Using Machine Learning Combined with Genetic Algorithm Optimization

Zhai

Zhao

Bai

et al. 2022

Systems

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Studying the impact of green and high-quality development is of great significance to the healthy growth and sustainable development of enterprises. This paper discusses the influencing factors of the green and high-quality development of enterprises from the perspective of ownership structure and innovation ability, aiming to clarify the impact mechanism of these influencing factors on the green development of enterprises, and combined with emerging machine learning technologies, to propose a novel and effective corporate green high-quality development using a regression prediction model for quality development. Linear regression and one-way ANOVA were used to analyze the influence of each variable on the green and high-quality development of the enterprise, and the weight proportions of each influencing factor under the linear model were obtained. Two machine learning models based on the random forest (RF) algorithm and support vector machine algorithm were established, and the random parameters in the two machine learning algorithms were optimized by a genetic algorithm (GA). The reliability and accuracy of machine learning models and multivariate linear models were compared. The results show that the GA–RF model has superior regression performance compared with other prediction models. This paper provides a convenient machine learning model, which can quickly and effectively predict the green and high-quality development of enterprises, and provide help for enterprise decision-making and government policy formulation.

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Machine Learning-Based Investigation on the Impact of Chinese Venture Capital Institutions’ Performance: Evaluation Factors of Venture Enterprises to Venture Capital Institutions

Cited by 3 publications

References 31 publications

The effects of organizational learning on innovation and performance of venture capital-backed firms: the moderating effect of syndication

The effects of organizational learning on innovation and performance of venture capital-backed firms: the moderating effect of syndication

Research on the Effectiveness of Deep Learning−Based Agency Cost Suppression Strategy: A Case Study of State−Owned Enterprises in Mainland China

Effective Evaluation of Green and High-Quality Development Capabilities of Enterprises Using Machine Learning Combined with Genetic Algorithm Optimization

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